Memory-efficient processing of raid metadata bitmaps

ABSTRACT

A storage system comprises a plurality of storage nodes each comprising one or more storage devices and a processor coupled to a memory. The storage system is configured to store data blocks across the storage devices of the storage nodes utilizing a redundant array of independent disks (RAID) arrangement. At least a given one of the storage nodes is configured to store a plurality of RAID metadata bitmaps in persistent storage of the storage node so as to be available for a recovery operation in the event of a detected failure, to identify a particular subset of the RAID metadata bitmaps to be updated in conjunction with an additional operation other than the recovery operation, and to temporarily store the identified subset of the RAID metadata bitmaps in the memory of the storage node in a manner determined based at least in part on an operation type of the additional operation.

FIELD

The field relates generally to information processing systems, and moreparticularly to storage in information processing systems.

BACKGROUND

In many storage systems, data is distributed across multiple storagedevices in accordance with redundant array of independent disks (RAID)arrangements. Some RAID arrangements allow a certain amount of lost datato be rebuilt from parity information, typically in response to astorage device failure or other type of failure within the storagesystem. Unfortunately, conventional RAID approaches can be problematicin that such approaches tend to keep RAID metadata in memory and writethe RAID metadata to disk only for recovery purposes. This can lead tosignificant degradations in storage system performance.

SUMMARY

Illustrative embodiments provide techniques for memory-efficientprocessing of RAID metadata bitmaps in a storage array or other type ofstorage system. For example, such embodiments can avoid the above-noteddrawbacks of conventional approaches, providing highly efficientmechanisms for processing RAID metadata bitmaps of a RAID array. In someembodiments, particular ones of the RAID metadata bitmaps areselectively loaded into memory under specified conditions associatedwith operations other than recovery operations and once updated are thenwritten to disk. Such arrangements can substantially decrease the memory“footprint” needed for RAID metadata bitmap maintenance relative toconventional approaches that keep the RAID metadata bitmaps constantlyin memory and write it to disk only for recovery purposes. As a result,storage system performance is improved relative to the conventionalapproaches.

In one embodiment, a storage system comprises a plurality of storagenodes each comprising one or more storage devices and a processorcoupled to a memory. The storage system is configured to store datablocks across the storage devices of the storage nodes utilizing a RAIDarrangement. At least a given one of the storage nodes is configured tostore a plurality of RAID metadata bitmaps in persistent storage of thestorage node so as to be available for a recovery operation in the eventof a detected failure, to identify a particular subset of the RAIDmetadata bitmaps to be updated in conjunction with an additionaloperation other than the recovery operation, and to temporarily storethe identified subset of the RAID metadata bitmaps in the memory of thestorage node in a manner determined based at least in part on anoperation type of the additional operation.

These and other illustrative embodiments include, without limitation,apparatus, systems, methods and processor-readable storage media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one example of an information processingsystem within which one or more illustrative embodiments areimplemented.

FIG. 2 is a block diagram of another example of an informationprocessing system within which one or more illustrative embodiments areimplemented.

FIG. 3 is a block diagram illustrating an example of a RAID 6 array forimplementation in the FIG. 1 system or the FIG. 2 system.

FIG. 4 is a table showing distribution of data blocks in the FIG. 3 RAID6 array.

FIG. 5 is a block diagram illustrating another example of a RAID 6 arrayfor implementation in the FIG. 1 system or the FIG. 2 system.

FIG. 6 is a table showing distribution of data blocks in the FIG. 5 RAID6 array.

FIG. 7 is a table showing an example of a stripe of a RAID 6 array withfree pages and taken pages with which one or more illustrativeembodiments can be implemented.

FIG. 8 is a block diagram of a content addressable storage systemcomprising a plurality of storage nodes configured to implementmemory-efficient processing of RAID metadata bitmaps in an illustrativeembodiment.

FIG. 9 is a flow diagram of a process for memory-efficient processing ofRAID metadata bitmaps in an illustrative embodiment.

FIGS. 10A, 10B, 10C and 10D show examples of logical layer and physicallayer mapping tables in an illustrative embodiment.

FIGS. 11 and 12 show examples of processing platforms that may beutilized to implement at least a portion of an information processingsystem in illustrative embodiments.

DETAILED DESCRIPTION

Illustrative embodiments will be described herein with reference toexemplary information processing systems and associated computers,servers, storage devices and other processing devices. It is to beappreciated, however, that these and other embodiments are notrestricted to the particular illustrative system and deviceconfigurations shown. Accordingly, the term “information processingsystem” as used herein is intended to be broadly construed, so as toencompass, for example, processing systems comprising cloud computingand storage systems, as well as other types of processing systemscomprising various combinations of physical and virtual processingresources. An information processing system may therefore comprise, forexample, at least one data center or other cloud-based system thatincludes one or more clouds hosting multiple tenants that share cloudresources. Numerous different types of enterprise computing and storagesystems are also encompassed by the term “information processing system”as that term is broadly used herein.

FIG. 1 shows an information processing system 100 configured inaccordance with an illustrative embodiment. The information processingsystem 100 comprises a host device 102, which may comprise one of aplurality of host devices of a computer system. The host device 102communicates over a network 104 with first and second storage systems105-1 and 105-2, also denoted as Storage System 1 and Storage System 2,respectively. The storage systems 105-1 and 105-2 are collectivelyreferred to herein as storage systems 105. The host device 102 andstorage systems 105 may be part of an enterprise computing and storagesystem, a cloud-based system or another type of system.

The host device 102 and storage systems 105 illustratively compriserespective processing devices of one or more processing platforms. Forexample, the host device 102 and the storage systems 105 can eachcomprise one or more processing devices each having a processor and amemory, possibly implementing virtual machines and/or containers,although numerous other configurations are possible.

The host device 102 and the storage systems 105 can additionally oralternatively be part of cloud infrastructure such as an Amazon WebServices (AWS) system. Other examples of cloud-based systems that can beused to provide one or more of host device 102 and storage systems 105include Google Cloud Platform (GCP) and Microsoft Azure.

The host device 102 is configured to write data to and read data fromthe storage systems 105. The host device 102 and the storage systems 105may be implemented on a common processing platform, or on separateprocessing platforms. A wide variety of other types of host devices canbe used in other embodiments.

The host device 102 in some embodiments illustratively provides computeservices such as execution of one or more applications on behalf of eachof one or more users associated with the host device 102.

The term “user” herein is intended to be broadly construed so as toencompass numerous arrangements of human, hardware, software or firmwareentities, as well as combinations of such entities. Compute and/orstorage services may be provided for users under a platform-as-a-service(PaaS) model, an Infrastructure-as-a-Service (IaaS) model and/or aFunction-as-a-Service (FaaS) model, although it is to be appreciatedthat numerous other cloud infrastructure arrangements could be used.Also, illustrative embodiments can be implemented outside of the cloudinfrastructure context, as in the case of a stand-alone computing andstorage system implemented within a given enterprise.

The network 104 is assumed to comprise a portion of a global computernetwork such as the Internet, although other types of networks can bepart of the network 104, including a wide area network (WAN), a localarea network (LAN), a satellite network, a telephone or cable network, acellular network, a wireless network such as a WiFi or WiMAX network, orvarious portions or combinations of these and other types of networks.The network 104 in some embodiments therefore comprises combinations ofmultiple different types of networks each comprising processing devicesconfigured to communicate using Internet Protocol (IP) or othercommunication protocols.

As a more particular example, some embodiments may utilize one or morehigh-speed local networks in which associated processing devicescommunicate with one another utilizing Peripheral Component Interconnectexpress (PCIe) cards of those devices, and networking protocols such asInfiniBand, Gigabit Ethernet or Fibre Channel. Numerous alternativenetworking arrangements are possible in a given embodiment, as will beappreciated by those skilled in the art.

The storage systems 105 are accessible to the host device over thenetwork 104. The storage system 105-1 comprises a plurality of storagedevices 106-1 and an associated storage controller 108-1. Similarly, thestorage system 105-2 comprises a plurality of storage devices 106-2 andan associated storage controller 108-2. The storage devices 106-1 and106-2 are collectively referred to herein as storage devices 106. Thestorage controllers 108-1 and 108-2 are collectively referred to asstorage controllers 108.

The storage devices 106 illustratively comprise solid state drives(SSDs). Such SSDs are implemented using non-volatile memory (NVM)devices such as flash memory. Other types of NVM devices that can beused to implement at least a portion of the storage devices 106 includenon-volatile random access memory (NVRAM), phase-change RAM (PC-RAM) andmagnetic RAM (MRAM). These and various combinations of multipledifferent types of NVM devices may also be used.

However, it is to be appreciated that other types of storage devices canbe used in other embodiments. For example, a given storage system as theterm is broadly used herein can include a combination of different typesof storage devices, as in the case of a multi-tier storage systemcomprising a flash-based fast tier and a disk-based capacity tier. Insuch an embodiment, each of the fast tier and the capacity tier of themulti-tier storage system comprises a plurality of storage devices withdifferent types of storage devices being used in different ones of thestorage tiers. For example, the fast tier may comprise flash driveswhile the capacity tier comprises hard disk drives (HDDs). Theparticular storage devices used in a given storage tier may be varied inother embodiments, and multiple distinct storage device types may beused within a single storage tier. The term “storage device” as usedherein is intended to be broadly construed, so as to encompass, forexample, flash drives or other types of SSDs, HDDs, hybrid drives orother types of storage devices.

In some embodiments, at least one of the storage systems 105illustratively comprises a scale-out all-flash content addressablestorage array such as an XtremIO™ storage array from Dell EMC ofHopkinton, Mass. Other types of storage arrays, including by way ofexample VNX® and Symmetrix VMAX® storage arrays also from Dell EMC, canbe used to implement one or both of storage systems 105 in otherembodiments.

The term “storage system” as used herein is therefore intended to bebroadly construed, and should not be viewed as being limited to contentaddressable storage systems or flash-based storage systems. A givenstorage system as the term is broadly used herein can comprise, forexample, network-attached storage (NAS), storage area networks (SANs),direct-attached storage (DAS) and distributed DAS, as well ascombinations of these and other storage types, includingsoftware-defined storage.

Other particular types of storage products that can be used inimplementing storage systems 105 in illustrative embodiments includeall-flash and hybrid flash storage arrays such as Unity™,software-defined storage products such as ScaleIO™ and ViPR®, cloudstorage products such as Elastic Cloud Storage (ECS), object-basedstorage products such as Atmos®, and scale-out NAS clusters comprisingIsilon® platform nodes and associated accelerators, all from Dell EMC.Combinations of multiple ones of these and other storage products canalso be used in implementing a given storage system in an illustrativeembodiment.

In the FIG. 1 embodiment, the storage devices 106 implement one or moreRAID arrays, denoted as RAID array 110-1 for storage devices 106-1 ofstorage system 105-1 and RAID array 110-2 for storage devices 106-2 ofstorage system 105-2. The RAID arrays 110-1 and 110-2 may collectivelyform a single larger RAID array, with the RAID arrays 110 representingdifferent portions of that single larger RAID array, or may instead beviewed as representing entirely distinct and separate RAID arrays. TheRAID arrays 110 are assumed to store data in stripes across a pluralityof SSDs provided by the storage devices 106. The RAID arrays 110implement examples of what is more generally referred to herein as datastriping across a plurality of storage devices in a storage system.

The host device 102 in the FIG. 1 embodiment includes a parity datacomputation module 112 which provides logic and functionality forcomputing parity data in a storage system that implements data stripingacross a plurality of storage devices (e.g., in RAID arrays 110 onstorage devices 106). Parity data computation will be described infurther detail below. The host device 102 should also be understood toinclude additional modules and other components typically found inconventional implementations of computers, servers or other hostdevices, although such additional modules and other components areomitted from the figure for clarity and simplicity of illustration.

The host device 102 and storage systems 105 in the FIG. 1 embodiment areassumed to be implemented using at least one processing platform eachcomprising one or more processing devices each having a processorcoupled to a memory. Such processing devices can illustratively includeparticular arrangements of compute, storage and network resources.

The host device 102 and the storage systems 105 may be implemented onrespective distinct processing platforms, although numerous otherarrangements are possible. For example, in some embodiments at leastportions of the host device 102 and one or both of the storage systems105 are implemented on the same processing platform. The storage systems105 can therefore be implemented at least in part within at least oneprocessing platform that implements at least a portion of the hostdevice 102.

The term “processing platform” as used herein is intended to be broadlyconstrued so as to encompass, by way of illustration and withoutlimitation, multiple sets of processing devices and associated storagesystems that are configured to communicate over one or more networks.For example, distributed implementations of the system 100 are possible,in which certain components of the system reside in one data center in afirst geographic location while other components of the system reside inone or more other data centers in one or more other geographic locationsthat are potentially remote from the first geographic location. Thus, itis possible in some implementations of the system 100 for the hostdevice 102 and storage systems 105 to reside in different data centers.Numerous other distributed implementations of one or both of the hostdevice 102 and the storage systems 105 are possible. Accordingly, thestorage systems 105 can also be implemented in a distributed manneracross multiple data centers.

Additional examples of processing platforms utilized to implement hostdevices and/or storage systems in illustrative embodiments will bedescribed in more detail below in conjunction with FIGS. 11 and 12.

It is to be appreciated that these and other features of illustrativeembodiments are presented by way of example only, and should not beconstrued as limiting in any way.

Accordingly, different numbers, types and arrangements of systemcomponents such as host device 102, network 104, storage systems 105,storage devices 106, storage controllers 108, and RAID arrays 110 can beused in other embodiments.

It should be understood that the particular sets of modules and othercomponents implemented in the system 100 as illustrated in FIG. 1 arepresented by way of example only. In other embodiments, only subsets ofthese components, or additional or alternative sets of components, maybe used, and such components may exhibit alternative functionality andconfigurations. Additional examples of systems implementingfunctionality for parity data computation in accordance with datastriping will be described below.

FIG. 2 shows an information processing system 200 configured inaccordance with another illustrative embodiment. The informationprocessing system 200 comprises a computer system 201 that includes hostdevices 202-1, 202-2, . . . 202-N collectively referred to as hostdevices 202. The host devices 202 communicate over a network 204 with astorage system 205. The computer system 201 is assumed to comprise anenterprise computer system, cloud-based computer system or otherarrangement of multiple compute nodes associated with respective users.The host devices 202 of the computer system 201 in some embodimentsillustratively provide compute services such as execution of one or moreapplications on behalf of each of one or more users associated withrespective ones of the host devices 202.

Similar to the storage systems 105 of system 100, the storage system 205comprises storage devices 206, storage controller 208 and RAID array210. However, in this embodiment, the functionality for parity datacomputation associated with data striping in RAID array 210 isimplemented in the storage system 205, rather than in one of the hostdevices 202. Accordingly, the storage controller 208 in this embodimentcomprises parity data computation module 212, which is configured tooperate in substantially the same manner as that described above forcorresponding module 112 of the host device 102 in the system 100.

In some embodiments, functionality for parity data computationassociated with data striping can be implemented partially in a hostdevice and partially in the storage system. For example, U.S. patentapplication Ser. No. 16/049,185, filed Jul. 30, 2018 and entitled“Efficient Computation of Parity Data in Storage System ImplementingData Striping,” which is incorporated by reference herein in itsentirety, discloses arrangements for utilizing processing capabilitieson the storage devices 106 themselves to perform a portion of a paritydata computation, results of which are then used for parity datacomputations on the host device (e.g., module 112) and/or the storagecontroller (e.g., module 212). Accordingly, illustrative embodiments arenot limited to arrangements in which all such functionality isimplemented in a host device or a storage system, and thereforeencompass various hybrid arrangements in which the functionality isdistributed over one or more host devices and one or more storagesystems, each comprising one or more processing devices.

Illustrative data striping operations with associated parity datacomputations in accordance with RAID based techniques will now bedescribed in further detail in the context of the information processingsystems 100 and 200. However, it is to be understood that data stripingoperations with associated parity data computations are more generallyapplicable to other types of information processing systems. At leastsome of the parity data computation steps are illustratively performedunder the control of the parity data computation module 112 in hostdevice 102 of system 100 or in module 212 in storage controller 208 ofsystem 200.

Data striping in some embodiments is implemented utilizing RAID, such asvia RAID arrays 110 on storage systems 105. In such embodiments, thenumber of data disks in the RAID storage system may comprise a primenumber k, and a column of the RAID storage system comprises k−1 blocks.The storage devices of the RAID storage system may be SSDs. The RAIDstorage system may implement RAID 6 with the number of data disks beingk and the number of parity disks being n, where n is greater than one(e.g., where n=2). In some embodiments, the stripe column size isselected as a multiple of a designated block size. The multiple may be aprime number minus 1. The prime number may be the same as or differentthan the prime numbers selected for different ones of the stripes.

In some cases, the prime number selected for a particular stripe may begreater than a number of the plurality of storage devices in the storagesystem that store data blocks for that stripe. To handle suchsituations, the parity blocks for the stripe may be computed by assumingor setting a set of virtual storage devices with pages storingdesignated predetermined values (e.g., zero pages). The particularnumber of virtual storage devices in the set may be equal to thedifference between the prime number selected for that stripe and thenumber of storage devices in the storage system which store data blocksfor that stripe.

The term RAID, as used herein, is an umbrella term for computer datastorage schemes that can divide and replicate data among multiplephysical disk drives. References to one or more “disks” in illustrativeembodiments disclosed herein are intended to be broadly construed, andare not limited to HDDs or other rotational media. For example, “disks”as that term is broadly used herein is intended to encompass other typesof storage drives such as SSDs. The terms “disks” and “drives” willtherefore be used interchangeably herein. The physical disks are said tobe in a RAID array, which is accessed by an operating system as onesingle disk. The different schemes or architectures are named by theword RAID followed by a number (e.g., RAID 0, RAID 1, etc.). Each schemeprovides a different balance between the goals of increasing datareliability and increasing input/output performance.

The RAID 6 scheme was developed to provide functionality for recoveringfrom multiple disk failures (e.g., similar to RAID 1.3) with highutilization rates (e.g., comparable to RAID 4 and 5) that avoids systembottlenecks. RAID 6 uses an N+2 parity scheme, which allows failure oftwo disks, where N is the number of disks in the array. RAID 6 definesblock-level striping with double distributed parity and provides faulttolerance of two drive failures, so that the array continues to operatewith up to two failed drives, irrespective of which two drives fail.

There are various implementations of RAID 6, which may use varyingcoding schemes. As the term is used herein, RAID 6 is defined as any N+2coding scheme which tolerates double disk failure, while user data iskept in the clear. This additional requirement assures that user readsare not affected by the RAID scheme under normal system operation.Examples of RAID 6 schemes include those that utilize the Reed Solomonerror correction code and those that utilize parity bits, such as thosewherein N data disks are supported by two redundancy disks each holdinga different parity bit. It should be noted that if all parity bits areon the same two disks, then the performance may be subject tobottlenecks. This can be solved by use of distributed parity stripesover N+2 disks similar to that specified in RAID 5. Examples of codingschemes based on parity calculations of rows and diagonals in a matrixof blocks include Even/Odd and Row Diagonal Parity (RDP). Both of theseschemes utilize a first parity disk P that holds the parities of rows ofblocks as well as a second parity disk Q that contains blocks that holdthe parity of diagonals of data blocks. In both schemes, it isadvantageous to work with a block size that is smaller than the nativepage size. For example, the native page size may be 8 KB, while theblock size is smaller but evenly divisible into 8 KB, e.g., 0.5 KB, 1KB, 2 KB, 4 KB. In an example where the native page size is 8 KB and theblock size is 2 KB, each stripe thus may contain four rows, and thus thefour blocks present on each disk form a single native page. However, astripe can also be defined by multiple rows of blocks distributed acrossthe storage devices of the RAID array. It is assumed that pages are readand written using a single disk operation.

FIG. 3 shows a RAID array 300, which in this example includes five datadisks denoted D0 through D4. A storage controller (e.g., such as storagecontrollers 108 or storage controller 208) is configured for writinginitial data into the array 300, and for updating existing data in thearray 300. The storage controller further provides functionality forrecovering data after single or double disk failure.

Each of the disks in the array 300 stores a column of data blocks. Thesame data block in successive disks forms a row, which is to say therows cross the disks. The data storage blocks are stored alongsideparity data blocks in parity disks denoted P and Q, and the numbers ofdata blocks in the different columns or disks may be different. Rowparity blocks are placed in a row parity column in disk P, and thediagonal parity data is placed in diagonal parity blocks in disk Q. Notethat parity data stored in parity disks P and Q is computed inaccordance with parity data computation module 112 (FIG. 1 system) orparity data computation module 212 (FIG. 2 system).

In the case of five data columns and four data rows, the number ofdiagonals is one greater than the number of rows. Thus, the diagonalparity column in disk Q includes one more block than the other columnsfor disks D0 through D4 and the row parity disk P. This is illustratedin FIG. 3 as Q is “taller” than D0 through D4 and P.

The number of data columns is a prime number, and the number of rows isone less than that prime number (e.g., in the FIG. 3 example the primenumber is 5 corresponding to the five data disks D0 through D4). Itshould be noted that, in practice, the various columns are distributedover the available physical disks to avoid system bottlenecks.

FIG. 4 shows a table 400 illustrating one example distribution of datablocks in the RAID array 300. In this case, there are k data disks,where k=5 is a prime number and there are five data columnscorresponding to disks D0 through D4. There are four rows (e.g., k−1).The P column includes the same four rows as the data columns D0 throughD4, but the Q column has an extra row.

In one example, each stripe is considered to contain k (where k must beprime) data columns D0 through D4, and two parity columns P and Q. Thestripe is composed of a quasi-matrix of blocks, which contains k−1 rows.Column P contains k−1 blocks, each providing the parity of the k datadisk blocks in its row. The k by k−1 matrix made up of the blocks in thedata columns includes k diagonals each of size k−1. Column Q, incontrast with the rest of the columns, contains k blocks and not k−1.Each of the k blocks in disk Q holds the parity of one of the kdiagonals. It should be noted that the ordering of blocks within eachcolumn may be arbitrary. Furthermore, the extra block in column Q may beplaced in a data column which does not contain a data block in thediagonal of which this block is the parity. Also, some of the rows maybe blank.

It should be appreciated that there are various other ways to distributedata blocks in an array such as RAID array 300. For example, in somecases it may be desired to provide more than one row parity column,which results in higher capacity overhead but which allows for a fasterrebuild after a single disk failure.

Additional details regarding the above-described techniques of FIGS. 3and 4 for storing data in RAID arrays are disclosed in U.S. Pat. No.9,552,258, entitled “Method and System for Storing Data in RAID MemoryDevices,” which is incorporated by reference herein.

FIG. 5 shows a RAID array 500 which, similar to RAID array 300 in FIG.3, includes five data disks denoted D0 through D4. Similarly, a storagecontroller (e.g., such as storage controllers 108 or storage controller208) is configured for writing initial data into the array 500, and forupdating existing data in the array 500. The storage controller furtherprovides functionality for recovering data after single or double diskfailure.

Similar to array 300, each of the disks in the array 500 stores a columnof data blocks. The same data block in successive disks forms a row.Further, the data storage blocks are stored alongside parity data blocksin parity disks denoted P and Q, where row parity blocks are placed in arow parity column in disk P, and the diagonal parity data is placed inparity blocks in disk Q. Note again that parity data stored in paritydisks P and Q is computed in accordance with parity data computationmodule 112 (FIG. 1 system) or parity data computation module 212 (FIG. 2system).

Recall that in array 300, the diagonal parity column in disk Q includesone more block than the other columns for disks D0 through D4 and therow parity disk P (i.e., in the case of five data columns and four datarows, the number of diagonals is one greater than the number of rows).However, in array 500, Q has the same number of blocks as D0 through D4and P and therefore is the same size as the other disks, as illustratedin FIG. 5. This is because array 500 utilizes “column parity data” thatis computed and combined with diagonal parity data to eliminate theextra block in disk Q. Computation of column parity data will be furtherexplained below.

FIG. 6 shows a table 600 illustrating one example distribution of datablocks in the RAID array 500. The implementation of FIGS. 5 and 6 isreferred to as an updated RAID 6 implementation. In this case, as withtable 400 for array 300, there are k data disks, where k=5 is a primenumber and there are five data columns corresponding to disks D0 throughD4. There are four rows (e.g., k−1). The P column includes the same fourrows as the data columns D0 through D4, but in this embodiment, unliketable 400, the Q column has the same number of rows as D0 through D4 andP.

In table 600, row parity data and diagonal parity data are computed asdescribed above with respect to table 400. However, parity computationin the embodiment of FIGS. 5 and 6 avoids the extra block in disk Q byadding per column parity to the diagonals. This approach introduces anadditional cost of the need to calculate the column parity. Columnparity provides an updated RAID 6 scheme as follows:

Let S be a stripe and mark S_(i,j):=the block in row i column j.

For every disk j column of the stripe, let d_(j):=⊕_(i=1) ^(p−1)S_(i,j)∀i ∈{1 . . . p−1}.

Define p_(i):=parity of row i and q_(i):=parity of diagonal i. Bydiagonal i, we refer to the diagonal that is not intersecting withcolumn i (as explained above, non-existent columns are just consideredas predefined zeros).

Let q_(p) be the extra Q block.

Define q _(k):=q_(k)⊕d_(k) where the symbol ⊕ refers to an XORoperation. The XOR operation is a Boolean operation used at the binarylevel to create RAID parity. This operation is the exclusive disjunctionoperation also known as exclusive OR (XOR). In the XOR operation (alsoreferred to as XOR'ing, performing XOR, XOR computation, etc.), binarydata is passed through logic that embodies the XOR operation and resultsin a binary result, which is used for redundancy and error correction asdescribed herein. In such case, the result of the XOR operation isreferred to as parity data.

Thus, q_(k) is referred to in table 600 as “diagonal parity” and d_(k)is referred to as “column parity.” As such, q_(k)⊕d_(k) is referred toin table 600 as “diagonal parity and column parity.” Further, disk Q isdenoted as Q in table 600.

Thus, given P and Q, the updated RAID 6 implementation described inFIGS. 5 and 6 enables recovery from a double failure. In variousembodiments, if a data disk and a Q parity disk fails, recovery in anupdated RAID 6 implementation continues as normal.

In certain embodiments, if data disk i≠p and disk P fail, d₁ is knownfor all i≠j, and d_(i) can be recovered from q _(i) since the diagonal iis not intersecting column i thus q_(i) is known. In some embodiments,XOR'ing out the d_(j) from Q bring us to the known recovery formula. Incertain embodiments, if i=p then Q is known since all d_(j) are known,and each block may be recovered from the diagonal.

In some embodiments, if two data disks fail (disk i and disk j) whereneither failed disk is the parity disk P, the updated RAID 6implementation enables recovery of the blocks using two steps. In one ormore embodiments, a first step includes partially recovering each block.

Ŝ _(k,i) =Ŝ _(k,i)⊕{either d _(i) or d _(j)}

Ŝ _(k,j) =Ŝ _(k,j)⊕{either d _(i) or d _(j)}

In one or more embodiments, a second step includes XOR'ing out d_(i) andd_(j) to get the data block S_(k,i) and S_(k,j). Since p−1 is even, rand p−1−r are even/odd together, if r is even then an XOR on all blocksof the column i eliminates d_(j) and d_(i) and thus we will get d_(i)and in the same way we can get d_(j), and thus recover the data. If r isodd, then we get ⊕_(i=1) ^(r)S_(k,j)⊕_(i=r+1)^(p−1−r)S_(k,j)⊕d_(j)⊕d_(i)=d_(i) thus we get d_(i) and d_(j) and we maycontinue recovery. In some embodiments, the case of i=p is just aspecial case with r=0.

In one or more embodiments, an updated RAID 6 implementation such asthat depicted in FIGS. 5 and 6 provides many advantages. For example, aparity disk failure causes, at most, reading 2*P+1 blocks fordouble-degraded read. Still further, upon write of a single block, atmost three parities are be updated (row parity, diagonal parity, and thediagonal XOR'd with the column parity).

Note again that, in some embodiments, parity data stored in parity disksP and Q, including column parity data and the combined diagonal parityand column parity data, is computed in accordance with parity datacomputation module 112 (FIG. 1 system) or parity data computation module212 (FIG. 2 system).

Additional details regarding the above-described techniques of FIGS. 5and 6 for storing data in RAID arrays are disclosed in U.S. Pat. No.9,891,994, entitled “Updated RAID 6 Implementation,” which isincorporated by reference herein.

FIG. 7 shows a table 700 illustrating another example data stripingdistribution with column parity data combined with diagonal parity dataas explained above in the context of FIGS. 5 and 6. In table 700, astripe S is depicted with prime equal to 41 and 34 data disks SSD 1through SSD 34. Note that it is assumed that the disks are SSDs.Further, SSD 35 is the row parity disk (disk P above) and SSD 36 is thecombined diagonal party and column parity disk (disk Q above). Note thateach block in the data disks is labeled either T for taken space (notavailable since the space contains data) or F for free space (availablespace since the space does not contain data).

One approach for calculating the column parity d_(j) (column paritydata) is: reading the column from the drive and performing the XORoperations with the diagonal parity data, as explained above, in thehost device (e.g., module 112 in FIG. 1) or the storage controller(e.g., module 212 in FIG. 2); adding new pages instead of the old pages;and keeping all the rest of the data pages as they are. This introducessignificant read amplification in case the stripe is not empty, as wellas CPU consumption for the parity calculations (XOR operations).

For example, assume a page size is 8 KB, and a stripe has 40 rows and 34data columns as in table 700, and assume the stripe is fifty percentfull (50% of the blocks are non-free or taken (T) and 50% are free (F)).Then, there are about 680 pages free (pages that can be overwritten). Ifthey are well distributed across the SSDs, this means there are 20 freepages in each column. For each column, the entire 20 (i.e., 40 minus 20)pages have to be read to recalculate the column (for the stripe, 680pages in total need to be read), resulting in an additional readoperation for every page to update, as well as additional XOR operationsof every column. It is also realized that the read amplification getsworse as the stripe is fuller.

An alternative to reading the entire column from the drive is to savethe column data which is optimal in terms of bandwidth and CPUconsumption but is very wasteful in terms of SSD capacity.

Illustrative embodiments described in the above-cited U.S. patentapplication Ser. No. 16/049,185 overcome the above and other drawbacksassociated with column parity data computations by utilizing processingcapabilities of the storage devices (e.g., SSDs) themselves to performat least a portion of the column parity data computations. For example,SSDs illustratively comprise flash drives that are equipped withprocessing capabilities such as, for example, an internal centralprocessing unit (CPU) and/or a hardware accelerator unit. One or moresuch processing capabilities are referred to herein as “a processingdevice” of the SSD. As described in the above-cited U.S. patentapplication Ser. No. 16/049,185, such processing capabilities of theSSDs are used to perform operations over data in the drives withoutinvolving the host device (e.g., 102 in FIG. 1) or storage controllers(e.g., 108 in FIG. 1 or 208 in FIG. 2). The results of the operationsperformed by the internal processing capabilities of the SSDs are madeavailable to the host device or storage controllers to perform furtheroperations.

More particularly, optimization of the column calculations leverages SSDprocessing capabilities which, in turn, optimizes both the bandwidth tothe drives, as well as saving the CPU of the host device or storagecontroller needed for calculating the column parity.

For each column (i.e., a column of blocks resides on a specific drive),a command (instruction) is sent from a parity data computation module(112 in host device 108 or module 212 in storage controller 208) to thegiven drive to calculate the XOR result of all non-free pages in thecolumn using internal CPU and/or hardware acceleration engines of thegiven drive. The result of the XOR operation is read from the givendrive by the parity data computation module (112 or 212). Then, bycombining the results read from the given drive with the new data to bewritten to the free space, the column XOR operation is completed.Additional details can be found in the above-cited U.S. patentapplication Ser. No. 16/049,185.

Additional illustrative embodiments will now be described. Theseembodiments may but need not utilize the above-described paritycomputation techniques, and can be implemented in a wide variety ofother types of storage systems using different RAID and paritycomputation arrangements.

The illustrative embodiments to be described below in conjunction withFIGS. 8, 9 and 10A-10D provide techniques for memory-efficientprocessing of RAID metadata bitmaps in a storage array or other type ofstorage system.

As indicated previously, these embodiments provide significantadvantages over conventional approaches that keep the RAID metadatabitmaps constantly in memory and write the RAID metadata bitmaps to diskonly for recovery purposes. For example, some of the embodiments to bedescribed provide highly efficient mechanisms for processing RAIDmetadata bitmaps of a RAID array. In some embodiments, particular onesof the RAID metadata bitmaps are selectively loaded into memory underspecified conditions associated with operations other than recoveryoperations and once updated are then written to disk. Such arrangementscan substantially decrease the memory “footprint” needed for RAIDmetadata bitmap maintenance relative to conventional approaches thatkeep the RAID metadata bitmaps constantly in memory and write it to diskonly for recovery purposes. As a result, storage system performance isimproved relative to the conventional approaches.

In some of these embodiments, a storage system comprises a plurality ofstorage nodes each comprising one or more storage devices, with each ofthe storage nodes further comprising a processor coupled to a memory.The storage system is configured to store data blocks across the storagedevices of the storage nodes utilizing a RAID arrangement such as thosedescribed previously herein, or other types of RAID arrangements. Forexample, such a storage system illustratively comprises a cluster ofstorage nodes implementing a RAID array. The RAID array comprises one ormore storage devices on each of the storage nodes, and each storage nodeis illustratively running software responsible for a portion of thecompute power and drives of the RAID array.

One or more of the storage nodes are each configured to store aplurality of RAID metadata bitmaps in persistent storage of the storagenode so as to be available for a recovery operation in the event of adetected failure, to identify a particular subset of the RAID metadatabitmaps to be updated in conjunction with an additional operation otherthan the recovery operation, and to temporarily store the identifiedsubset of the RAID metadata bitmaps in the memory of the storage node ina manner determined based at least in part on an operation type of theadditional operation. It is assumed that the remaining RAID metadatabitmaps are not stored in the memory of the storage node, but insteadremain stored in the persistent storage of that storage node. Thepersistent storage illustratively comprises one or more of the “disks”or other storage devices of the storage node. This is in contrast to thememory of the storage node, illustratively implemented as electronicmemory.

As previously described herein, the RAID arrangement is assumed toinclude parity information supporting at least one recovery option forreconstructing the data blocks of at least one of the storage devicesresponsive to a failure of that storage device.

Each of the storage nodes in some embodiments further comprises a set ofprocessing modules configured to communicate over one or more networkswith corresponding sets of processing modules on other ones of thestorage nodes. The sets of processing modules illustratively compriserespective servers that collectively implement at least a portion of adistributed storage controller of the storage system. As anotherexample, the sets of processing modules of the storage nodes eachcomprise at least one data module and at least one control module, aswell as additional modules, examples of which are described in moredetail elsewhere herein.

The given storage node in reading data blocks from its one or morestorage devices is illustratively configured to read the data blocksfrom at least one designated RAID stripe. The data blocks in someembodiments comprise respective data pages.

Different ones of the RAID metadata bitmaps indicate at least free ortaken status for different subsets of the data blocks stored across thestorage devices of the storage nodes. A given one of the RAID metadatabitmaps illustratively comprises a plurality of entries for respectiveones of its associated data blocks, with each such entry indicatingwhich of a plurality of possible states applies to the correspondingdata block wherein the possible states include at least a free state anda taken state. The possible states in some embodiments further compriseat least one additional state, such as a “not parity” state. Other typesand arrangements of RAID metadata bitmaps can be used in otherembodiments.

As will be described in more detail below, the storage nodes areconfigured to save RAID metadata bitmaps to disk or other persistentstorage not only for recovery purposes but also in conjunction withother types of processing of IO operations in the storage system. Theseembodiments address different scenarios for which the RAID metadatabitmaps are used how the RAID metadata bitmaps are handled to avoidcausing performance degradations such as those that are typical ofconventional approaches.

In some embodiments, identifying a particular subset of the RAIDmetadata bitmaps to be updated in conjunction with an additionaloperation other than the recovery operation comprises detecting a writecache emptying operation, determining particular RAID stripes to bewritten as part of the write cache emptying operation, selecting one ormore of the particular RAID stripes, and identifying the particularsubset of the RAID metadata bitmaps as one or more of the RAID metadatabitmaps associated with the selected one or more RAID stripes. Selectingone or more of the RAID stripes illustratively comprises selecting theone or more RAID stripes based at least in part on amounts of free datablocks in respective ones of the RAID stripes, although otherarrangements are possible.

As a more particular example, when the storage system writes user datato RAID stripes, the corresponding RAID metadata bitmaps are read inorder to determine which data pages can be written. This reading of RAIDmetadata bitmaps does not impact user latency since it can be performedas part of a background process. However, it is also possible toprefetch selected RAID metadata bitmap pages for the next X stripeshaving the most free data pages. Such an arrangement illustrativelycreates a moving window of X pages ahead of the RAID stripe currentlybeing written. Once the RAID stripe is written, the corresponding RAIDmetadata bitmap pages are written back to disk or other persistentstorage.

Additionally or alternatively, some embodiments are configured such thatthe given storage node selects particular ones of a plurality of RAIDstripes to be rebuilt in a rebuild operation, reads one or more of theRAID metadata bitmaps associated with the selected RAID stripes into thememory, performs corresponding portions of the rebuild operation, writesthe RAID metadata bitmaps to the persistent storage, and repeats theselecting, reading, performing and writing until the rebuild operationis complete. The rebuild operation can be advantageously configured toexhibit a high level of RAID metadata bitmap locality, as the storagesystem can select which RAID stripes to rebuild. For example, thestorage node can read a RAID metadata bitmap page, rebuild all of theRAID stripes represented by that page, and then write the RAID metadatabitmap page to persistent storage.

In some embodiments, the given storage node is illustratively configuredto delay verification of at least one of the RAID metadata bitmaps inconjunction with a read operation, and to perform the verification ofthe one or more RAID metadata bitmaps in conjunction with a subsequentwriting of those RAID metadata bitmaps.

For example, such an arrangement can be configured to skip verificationfor RAID metadata bitmap pages that are marked as taken, and to store anindication that the pages were read and that the verification will beperformed later during a write to those pages. In some implementations,the correctness of a given such RAID metadata bitmap page is verified inconjunction with a write to the corresponding RAID stripe, or any otheraccess to the given RAID metadata bitmap page that involves reading andwriting that page.

In some embodiments, identifying a particular subset of the RAIDmetadata bitmaps to be updated in conjunction with an additionaloperation other than the recovery operation comprises detecting anoperation that involves decrementing reference counts of a subset of thedata blocks to zero, and identifying the particular subset of the RAIDmetadata bitmaps as one or more of the RAID metadata bitmaps associatedwith the operation that involves decrementing reference counts of asubset of the data blocks to zero.

Decrementing the reference count of a given data page or other datablock to zero illustratively requires updating the corresponding entryof its RAID metadata bitmap to indicate that the status of the givendata block is free. Such reference count decrementing operations canaccumulated for improved locality and enhanced performance usingtechniques such as those disclosed in U.S. Pat. No. 10,261,693, entitled“Storage System with Decoupling and Reordering of Logical and PhysicalCapacity Removal,” which is incorporated by reference herein. Thesetechniques can be applied in illustrative embodiments herein tofacilitate reading of RAID metadata bitmap pages, updating of thosepages in accordance with the reference count decrementing operations,and then writing of the updated pages.

In some embodiments, the given storage node is illustratively configuredto identify a subset of the data blocks to be read in conjunction with areconstruction operation to correct data block degradations, and tolaunch multiple reads of respective RAID metadata bitmaps in parallelwith performing reads of the identified subset of data blocks.

For example, these embodiments address situations involving a read of adegraded page, such as a page that is on a failed disk of the RAIDarrangement. In these and other situations, the information in one ormore RAID metadata bitmaps is used in order to reconstruct a degradedpage. In some implementations, the storage node is configured to read aRAID metadata bitmap, and then to read all the pages that are needed forreconstruction of the degraded page according to the bitmap, althoughsuch an approach may increase user latency. To avoid this potentiallatency, the storage node can instead be configured to launch the readsof the RAID metadata bitmap in parallel with the data reads, althoughthis may result in some pages that are not needed for thereconstruction.

Referring now to FIG. 8, an example storage system of the type describedabove is shown in more detail. This figure shows a content addressablestorage system 805 comprising a plurality of storage devices 806 and anassociated storage controller 808. The content addressable storagesystem 805 may be viewed as a particular implementation of a given oneof the storage systems 105 or 205, and accordingly is assumed to becoupled to one or more host devices such as host device 102 or hostdevices 202. The content addressable storage system 805 is configured toimplement functionality for memory-efficient processing of RAID bitmapsassociated with the storage devices 806 in an illustrative embodiment.

The storage controller 808 includes distributed modules 812, 814 and816, illustratively distributed across a plurality of storage nodes 815of the content addressable storage system 805. The content addressablestorage system 805 in the FIG. 8 embodiment is therefore implemented asa clustered storage system comprising storage nodes 815 each of whichcomprises a corresponding subset of the storage devices 806. Otherclustered storage system arrangements comprising multiple storage nodesand possibly additional or alternative nodes can be used in otherembodiments. A given clustered storage system may therefore include notonly storage nodes 815 but also additional storage nodes, compute nodesor other types of nodes coupled to a network such as network 104 ornetwork 204. Alternatively, such additional storage nodes may be part ofanother clustered storage system of the system 100 or 200. Each of thestorage nodes 815 of the storage system 805 is assumed to be implementedusing at least one processing device comprising a processor coupled to amemory.

The distributed modules 812, 814 and 816 more particularly comprisedistributed parity data computation module 812, distributed bitmapupdate control logic 814, and distributed in-memory RAID metadatabitmaps 816. It is assumed that each of the distributed modules 812, 814and 816 comprises multiple module instances implemented on respectiveones of the storage nodes 815. These module instances are furtherassumed to be part of the storage controller 808. The distributedmodules 812, 814 and 816 collectively implement the memory-efficientprocessing of RAID metadata bitmaps algorithm shown in the flow diagramof FIG. 9. Additionally or alternatively, such modules are configured toimplement other types of functionality for memory-efficient processingof RAID metadata bitmaps.

The instance of module 816 on a given one of the storage nodes 815illustratively represents a particular subset of the RAID metadatabitmaps of that storage node that are temporarily stored in a memory ofthat storage node, while the remaining RAID metadata bitmaps are assumedto remain stored in one or more disks or other types of persistentstorage of that storage node, with such persistent storage in thisembodiment being represented by a corresponding portion of the storagedevices 806. The particular subset of the RAID metadata bitmaps that isstored in the instance of module 816 changes dynamically over time asdifferent types of operations are executed within the storage system805, so as to implement memory-efficient processing of the RAID metadatabitmaps in the manner described elsewhere herein. The instance of module816 therefore represents changing sets of RAID metadata bitmaps inmemory of the corresponding storage node.

As indicated above, the storage controller 808 of the contentaddressable storage system 805 is implemented in a distributed manner soas to comprise a plurality of distributed storage controller componentsimplemented on respective ones of the storage nodes 815. The storagecontroller 808 is therefore an example of what is more generallyreferred to herein as a “distributed storage controller.” In subsequentdescription herein, the storage controller 808 is referred to asdistributed storage controller 808.

Each of the storage nodes 815 in this embodiment further comprises a setof processing modules configured to communicate over one or morenetworks with corresponding sets of processing modules on other ones ofthe storage nodes 815. The sets of processing modules of the storagenodes 815 collectively comprise at least a portion of the distributedstorage controller 808 of the content addressable storage system 805.

The modules of the distributed storage controller 808 in the presentembodiment more particularly comprise different sets of processingmodules implemented on each of the storage nodes 815. The set ofprocessing modules of each of the storage nodes 815 comprises at least acontrol module 808C, a data module 808D and a routing module 808R. Thedistributed storage controller 808 further comprises one or moremanagement (“MGMT”) modules 808M. For example, only a single one of thestorage nodes 815 may include a management module 808M. It is alsopossible that management modules 808M may be implemented on each of atleast a subset of the storage nodes 815. A given set of processingmodules implemented on a particular one of the storage nodes 815therefore illustratively includes at least one control module 808C, atleast one data module 808D and at least one routing module 808R, andpossibly a management module 808M.

Terms such as “control module,” “data module,” “routing module” and“management module” as used herein are intended to be broadly construed,so as to encompass, for example, different processing modules of aserver implemented by a storage node running on a processing device of aclustered storage system. It is also possible that such modules maycomprise respective separate nodes or other separate processing devicesof a clustered storage system.

Communication links may be established between the various processingmodules of the distributed storage controller 808 using well-knowncommunication protocols such as IP, Transmission Control Protocol (TCP),and remote direct memory access (RDMA). For example, respective sets ofIP links used in data transfer and corresponding messaging could beassociated with respective different ones of the routing modules 808R.

Although shown as separate modules of the distributed storage controller808, the modules 812, 814 and 816 in some embodiments may be distributedat least in part over at least a subset of the other modules 808C, 808D,808R and 808M of the storage controller 808. Accordingly, at leastportions of the functionality for memory-efficient processing of RAIDmetadata bitmaps functionality of the modules 812, 814 and 816 may beimplemented in one or more of the other modules of the storagecontroller 808. In other embodiments, the modules 812, 814 and 816 maybe implemented as stand-alone modules of the storage controller 808.

The storage devices 806 are configured to store metadata pages 820 anduser data pages 822, and may also store additional information notexplicitly shown such as checkpoints and write journals. The metadatapages 820 and the user data pages 822 are illustratively stored inrespective designated metadata and user data areas of the storagedevices 806. Accordingly, metadata pages 820 and user data pages 822 maybe viewed as corresponding to respective designated metadata and userdata areas of the storage devices 806.

A given “page” as the term is broadly used herein should not be viewedas being limited to any particular range of fixed sizes. In someembodiments, a page size of 8 kilobytes (KB) is used, but this is by wayof example only and can be varied in other embodiments. For example,page sizes of 4 KB, 16 KB or other values can be used. Accordingly,illustrative embodiments can utilize any of a wide variety ofalternative paging arrangements for organizing the metadata pages 820and the user data pages 822.

The user data pages 822 are part of a plurality of LUNs configured tostore files, blocks, objects or other arrangements of data, each alsogenerally referred to herein as a “data item,” on behalf of users of thecontent addressable storage system 805. Each such LUN may compriseparticular ones of the above-noted pages of the user data area. The userdata stored in the user data pages 822 can include any type of user datathat may be utilized in the system 100. The term “user data” herein istherefore also intended to be broadly construed.

A given storage volume for which content-based signatures are generatedby storage controller 808 illustratively comprises a set of one or moreLUNs, each including multiple ones of the user data pages 822 stored instorage devices 806.

The content addressable storage system 805 in the embodiment of FIG. 8is configured to generate hash metadata providing a mapping betweencontent-based digests of respective ones of the user data pages 822 andcorresponding physical locations of those pages in the user data area.Content-based digests generated using hash functions are also referredto herein as “hash digests.” Such hash digests or other types ofcontent-based digests are examples of what are more generally referredto herein as “content-based signatures” of the respective user datapages 822. The hash metadata generated by the content addressablestorage system 805 is illustratively stored as metadata pages 820 in themetadata area. The generation and storage of the hash metadata isassumed to be performed under the control of the storage controller 808.

Each of the metadata pages 820 characterizes a plurality of the userdata pages 822. For example, in a given set of n user data pagesrepresenting a portion of the user data pages 822, each of the user datapages is characterized by a LUN identifier, an offset and acontent-based signature. The content-based signature is generated as ahash function of content of the corresponding user data page.Illustrative hash functions that may be used to generate thecontent-based signature include the SHA1 secure hashing algorithm, orother secure hashing algorithms known to those skilled in the art,including SHA2, SHA256 and many others. The content-based signature isutilized to determine the location of the corresponding user data pagewithin the user data area of the storage devices 806.

Each of the metadata pages 820 in the present embodiment is assumed tohave a signature that is not content-based. For example, the metadatapage signatures may be generated using hash functions or other signaturegeneration algorithms that do not utilize content of the metadata pagesas input to the signature generation algorithm. Also, each of themetadata pages is assumed to characterize a different set of the userdata pages.

A given set of metadata pages representing a portion of the metadatapages 820 in an illustrative embodiment comprises metadata pages havingrespective signatures. Each such metadata page characterizes a differentset of n user data pages. For example, the characterizing information ineach metadata page can include the LUN identifiers, offsets andcontent-based signatures for each of the n user data pages that arecharacterized by that metadata page. It is to be appreciated, however,that the user data and metadata page configurations described above areexamples only, and numerous alternative user data and metadata pageconfigurations can be used in other embodiments.

Ownership of a user data logical address space within the contentaddressable storage system 805 is illustratively distributed among thecontrol modules 808C.

The functionality for memory-efficient processing of RAID metadatabitmaps provided by modules 812, 814 and 816 in some embodiments can bedistributed across at least a subset of the processing modules 808C,808D, 808R and 808M of the distributed storage controller 808.

In some embodiments, the content addressable storage system 805comprises an XtremIO™ storage array suitably modified to incorporatefunctionality for memory-efficient processing of RAID metadata bitmapsas disclosed herein.

In arrangements of this type, the control modules 808C, data modules808D and routing modules 808R of the distributed storage controller 808illustratively comprise respective C-modules, D-modules and R-modules ofthe XtremIO™ storage array. The one or more management modules 808M ofthe distributed storage controller 808 in such arrangementsillustratively comprise a system-wide management module (“SYM module”)of the XtremIO™ storage array, although other types and arrangements ofsystem-wide management modules can be used in other embodiments.Accordingly, functionality for memory-efficient processing of RAIDmetadata bitmaps in some embodiments is implemented under the control ofat least one system-wide management module of the distributed storagecontroller 808, utilizing the C-modules, D-modules and R-modules of theXtremIO™ storage array.

In the above-described XtremIO™ storage array example, each user datapage has a fixed size such as 8 KB and its content-based signature is a20-byte signature generated using the SHA1 secure hashing algorithm.Also, each page has a LUN identifier and an offset, and so ischaracterized by <lun_id, offset, signature>.

The content-based signature in the present example comprises acontent-based digest of the corresponding data page. Such acontent-based digest is more particularly referred to as a “hash digest”of the corresponding data page, as the content-based signature isillustratively generated by applying a hash function such as the SHA1secure hashing algorithm to the content of that data page. The full hashdigest of a given data page is given by the above-noted 20-bytesignature. The hash digest may be represented by a corresponding “hashhandle,” which in some cases may comprise a particular portion of thehash digest. The hash handle illustratively maps on a one-to-one basisto the corresponding full hash digest within a designated clusterboundary or other specified storage resource boundary of a given storagesystem. In arrangements of this type, the hash handle provides alightweight mechanism for uniquely identifying the corresponding fullhash digest and its associated data page within the specified storageresource boundary. The hash digest and hash handle are both consideredexamples of “content-based signatures” as that term is broadly usedherein.

Examples of techniques for generating and processing hash handles forrespective hash digests of respective data pages are disclosed in U.S.Pat. No. 9,208,162, entitled “Generating a Short Hash Handle,” and U.S.Pat. No. 9,286,003, entitled “Method and Apparatus for Creating a ShortHash Handle Highly Correlated with a Globally-Unique Hash Signature,”both of which are incorporated by reference herein.

As mentioned previously, storage controller components in an XtremIO™storage array illustratively include C-module, D-module and R-modulecomponents. For example, separate instances of such components can beassociated with each of a plurality of storage nodes in a clusteredstorage system implementation.

The distributed storage controller in this example is configured togroup consecutive pages into page groups, to arrange the page groupsinto slices, and to assign the slices to different ones of theC-modules. For example, if there are 1024 slices distributed evenlyacross the C-modules, and there are a total of 16 C-modules in a givenimplementation, each of the C-modules “owns” 1024/16=64 slices. In sucharrangements, different ones of the slices are assigned to differentones of the control modules 808C such that control of the slices withinthe storage controller 808 of the storage system 805 is substantiallyevenly distributed over the control modules 808C of the storagecontroller 808.

The D-module allows a user to locate a given user data page based on itssignature. Each metadata page also has a size of 8 KB and includesmultiple instances of the <lun_id, offset, signature> for respectiveones of a plurality of the user data pages. Such metadata pages areillustratively generated by the C-module but are accessed using theD-module based on a metadata page signature.

The metadata page signature in this embodiment is a 20-byte signaturebut is not based on the content of the metadata page. Instead, themetadata page signature is generated based on an 8-byte metadata pageidentifier that is a function of the LUN identifier and offsetinformation of that metadata page.

If a user wants to read a user data page having a particular LUNidentifier and offset, the corresponding metadata page identifier isfirst determined, then the metadata page signature is computed for theidentified metadata page, and then the metadata page is read using thecomputed signature. In this embodiment, the metadata page signature ismore particularly computed using a signature generation algorithm thatgenerates the signature to include a hash of the 8-byte metadata pageidentifier, one or more ASCII codes for particular predeterminedcharacters, as well as possible additional fields. The last bit of themetadata page signature may always be set to a particular logic value soas to distinguish it from the user data page signature in which the lastbit may always be set to the opposite logic value.

The metadata page signature is used to retrieve the metadata page viathe D-module. This metadata page will include the <lun_id, offset,signature> for the user data page if the user page exists. The signatureof the user data page is then used to retrieve that user data page, alsovia the D-module.

Write requests processed in the content addressable storage system 805each illustratively comprise one or more IO operations directing that atleast one data item of the storage system 805 be written to in aparticular manner. A given write request is illustratively received inthe storage system 805 from a host device over a network. In someembodiments, a write request is received in the distributed storagecontroller 808 of the storage system 805, and directed from oneprocessing module to another processing module of the distributedstorage controller 808. For example, a received write request may bedirected from a routing module 808R of the distributed storagecontroller 808 to a particular control module 808C of the distributedstorage controller 808. Other arrangements for receiving and processingwrite requests from one or more host devices can be used.

The term “write request” as used herein is intended to be broadlyconstrued, so as to encompass one or more IO operations directing thatat least one data item of a storage system be written to in a particularmanner. A given write request is illustratively received in a storagesystem from a host device.

In the XtremIO™ context, the C-modules, D-modules and R-modules of thestorage nodes 815 communicate with one another over a high-speedinternal network such as an InfiniBand network. The C-modules, D-modulesand R-modules coordinate with one another to accomplish various IOprocessing tasks.

The write requests from the host devices identify particular data pagesto be written in the storage system 805 by their corresponding logicaladdresses each comprising a LUN ID and an offset.

As noted above, a given one of the content-based signaturesillustratively comprises a hash digest of the corresponding data page,with the hash digest being generated by applying a hash function to thecontent of that data page. The hash digest may be uniquely representedwithin a given storage resource boundary by a corresponding hash handle.The hash digest and the hash handle are each considered examples of whatare more generally referred to herein as content-based signatures of agiven data page. The hash digest and hash handle of a given data pageare also referred to as simply a “hash” of that data page. The datapages are examples of what are more generally referred to herein as“data blocks.”

Referring now to FIG. 9, an example process for memory-efficientprocessing of RAID metadata bitmaps in a RAID array comprising at leasta portion of the storage devices 806 of the content addressable storagesystem 805 is shown. It should be noted that the term “RAID array” asused herein is intended to be broadly construed, so as to encompass anystorage system that utilizes a RAID arrangement to distribute dataacross multiple storage devices. The process in this embodimentcomprises an example algorithm collectively implemented by thedistributed modules 812, 814 and 816. The process includes steps 900through 910, which are illustratively performed utilizing differentinstances of the modules 812, 814 and 816 deployed on respective ones ofthe storage nodes 815.

The steps are more specifically performed by a particular one of thestorage nodes, also referred to elsewhere herein as a given storagenode, possibly through interaction with other ones of the storage nodes.It is assumed that each of the other storage nodes also performsoperations similar to those performed by the first storage node. Any ofthe storage nodes can be the given storage node as that term is broadlyused herein.

Moreover, although steps 900-910 are shown in the figure as beingperformed in a particular sequence, this is by way of example only, forclarity and simplicity of illustration, and different ones of the stepsor sets of the steps can instead be performed concurrently, or otherwiseperformed in an asynchronous manner relative to one another. Theparticular configuration of the flow diagram should therefore not beviewed as indicating that synchronous performance of steps 900-910 isrequired.

References to a given storage nodes in the following description of theFIG. 9 process illustratively refers to a particular one of the storagenodes 815, and references to “disk” illustratively refer to subsets ofstorage devices 806 associated with the particular one of the storagenodes 815, although numerous other arrangements of storage nodes andassociated storage devices are possible in other embodiments. These andother references to “disk” or “disks” used herein, including within theterm RAID itself, should not be construed as limiting embodiments todisk drives or any other particular type of persistent storage.

In step 900, a given storage node detects a particular type of operationinvolving utilizing of RAID metadata bitmaps. For example, as describedelsewhere herein, the particular type of operation may comprise one of aplurality of designated operation types that are associated withmemory-efficient processing of RAID metadata bitmaps in a givenembodiment, such as write operations, read operations, RAID rebuildoperations, reference count decrementing operations and degraded pageread operations, although it is to be appreciated that other types ofoperations can be utilized in other embodiments.

In step 902, the storage node identifies a particular subset of the RAIDmetadata bitmaps to be updated in conjunction with the detectedoperation. The identification of the particular subset will generallyvary depending upon the particular type of operation detected, as wasdescribed in more detail above.

In step 904, the storage node retrieves one of more of the RAID metadatabitmaps identified in step 902 from disk into memory of the storagenode.

In step 906, the storage node updates the one or more retrieved RAIDmetadata bitmaps in conjunction with performance of the detectedoperation.

In step 908, a determination is made as to whether or not theperformance of the detected operation is complete. If the performance ofdetection operation is complete, the process moves to step 910, andotherwise returns to step 902 as indicated. In conjunction with a giveninstance of a return to step 902, at least portions of one or more ofsteps 902, 904 and 906 are repeated, illustratively using differentsubsets of the RAID metadata bitmaps, although other arrangements arepossible.

In step 910, the updated RAID metadata bitmaps are written back to disk.The process then returns to step 900 to detect and process anotheroperation. Alternatively, the writing of updated bitmaps back to diskcan occur as part of step 906, for example, as the bitmaps are updated.These and other features of the process can vary in some embodimentsdepending upon the particular type of operation detected in step 900.

As noted above, operations referred to above as being performed by onestorage node are assumed to be similarly performed by other ones of thestorage nodes.

Again, the sequential nature of the flow diagram should not be viewed asrequiring that the steps be performed synchronously. Instead,asynchronous performance of these steps is contemplated in manyimplementations. For example, different sets of the steps can beperformed as respective asynchronous tasks, and multiple parallelinstances of the process can be executed within a given storage node oracross multiple storage nodes of a storage system.

These and other example implementations of the FIG. 9 processadvantageously avoid the previously-described drawbacks of conventionalapproaches.

Illustrative embodiments of storage systems with memory-efficientprocessing of RAID metadata bitmaps as disclosed herein can thereforeprovide a number of significant advantages relative to conventionalarrangements, such as a substantially reduced memory footprint formaintenance of RAID metadata bitmaps and associated improved systemperformance, as described in more detail elsewhere herein.

The particular processing operations and other system functionalitydescribed above in conjunction with the flow diagram of FIG. 9 arepresented by way of illustrative example only, and should not beconstrued as limiting the scope of the disclosure in any way.Alternative embodiments can use other types of processing operations forimplementing memory-efficient processing of RAID metadata bitmaps in astorage system. For example, as indicated above, the ordering of theprocess steps may be varied in other embodiments, or certain steps maybe performed at least in part concurrently with one another rather thanserially. Also, one or more of the process steps may be repeatedperiodically, or multiple instances of the process can be performed inparallel with one another in order to support multiple instances ofmemory-efficient processing of RAID metadata bitmaps for multiplestorage systems or different portions of a single storage system.

Functionality such as that described in conjunction with the flowdiagram of FIG. 9 can be implemented at least in part in the form of oneor more software programs stored in memory and executed by a processorof a processing device such as a computer or server. As will bedescribed below, a memory or other storage device having executableprogram code of one or more software programs embodied therein is anexample of what is more generally referred to herein as a“processor-readable storage medium.”

A storage controller such as distributed storage controller 808 that isconfigured to control performance of one or more steps of the process ofthe flow diagram of FIG. 9 can be implemented as part of what is moregenerally referred to herein as a processing platform comprising one ormore processing devices each comprising a processor coupled to a memory.A given such processing device may correspond to one or more virtualmachines or other types of virtualization infrastructure such as Dockercontainers or Linux containers (LXCs). The host devices 102 or 202 andcontent addressable storage system 805, as well as other systemcomponents, may be implemented at least in part using processing devicesof such processing platforms. For example, in the distributed storagecontroller 808, respective distributed modules can be implemented inrespective containers running on respective ones of the processingdevices of a processing platform.

The FIG. 9 process makes use of various metadata structures that aremaintained within the content addressable storage system 805. Examplesof metadata structures maintained by the storage system in illustrativeembodiments include the logical layer and physical layer mapping tablesshown in respective FIGS. 10A, 10B, 10C and 10D. It is to be appreciatedthat these particular tables are only examples, and other tables ormetadata structures having different configurations of entries andfields can be used in other embodiments.

Referring initially to FIG. 10A, an address-to-hash (“A2H”) table 1000is shown. The A2H table 1000 comprises a plurality of entries accessibleutilizing logical addresses denoted Logical Address 1, Logical Address2, . . . Logical Address M as respective keys, with each such entry ofthe A2H table 1000 comprising a corresponding one of the logicaladdresses, a corresponding one of the hash handles, and possibly one ormore additional fields.

FIG. 10B shows a hash-to-data (“H2D”) table 1002 that illustrativelycomprises a plurality of entries accessible utilizing hash handlesdenoted Hash Handle 1, Hash Handle 2, . . . Hash Handle D as respectivekeys, with each such entry of the H2D table 1002 comprising acorresponding one of the hash handles, a physical offset of acorresponding one of the data pages, and possibly one or more additionalfields.

Referring now to FIG. 10C, a hash metadata (“HMD”) table 1004 comprisesa plurality of entries accessible utilizing hash handles denoted HashHandle 1, Hash Handle 2, . . . Hash Handle H as respective keys. Eachsuch entry of the HMD table 1004 comprises a corresponding one of thehash handles, a corresponding reference count and a correspondingphysical offset of one of the data pages. A given one of the referencecounts denotes the number of logical pages in the storage system thathave the same content as the corresponding data page and therefore pointto that same data page via their common hash digest. Although notexplicitly so indicated in the figure, the HMD table 1004 may alsoinclude one or more additional fields.

In the present embodiment, the HMD table of FIG. 10C illustrativelycomprises at least a portion of the same information that is found inthe H2D table of FIG. 10B. Accordingly, in other embodiments, those twotables can be combined into a single table, illustratively referred toas an H2D table, an HMD table or another type of physical layer mappingtable providing a mapping between hash values, such as hash handles orhash digests, and corresponding physical addresses of data pages.

FIG. 10D shows a physical layer based (“PLB”) table 1006 thatillustratively comprises a plurality of entries accessible utilizingphysical offsets denoted Physical Offset 1, Physical Offset 2, . . .Physical Offset P as respective keys, with each such entry of the PLBtable 1006 comprising a corresponding one of the physical offsets, acorresponding one of the hash digests, and possibly one or moreadditional fields.

As indicated above, the hash handles are generally shorter in lengththan the corresponding hash digests of the respective data pages, andeach illustratively provides a short representation of the correspondingfull hash digest. For example, in some embodiments, the full hashdigests are 20 bytes in length, and their respective corresponding hashhandles are illustratively only 4 or 6 bytes in length.

Collisions can arise where data pages with different content nonethelesshave the same hash handle. This is a possibility in embodiments thatutilize hash handles rather than full hash digests to identify datapages. Unlike the full hash digests which are generated usingcollision-resistant hash functions that can essentially guarantee uniquehash digests for data pages with different content, the hash handles canin some cases with very small probability lead to collisions. The hashhandle lengths and their manner of generation should therefore beselected so as to ensure that the collision probability is at or below amaximum acceptable level for the particular implementation.

It is to be appreciated that terms such as “table” and “entry” as usedherein are intended to be broadly construed, and the particular exampletable and entry arrangements of FIGS. 10A through 10D can be varied inother embodiments. For example, additional or alternative arrangementsof tables and entries can be used.

The content addressable storage system 805 utilizes a two-level mappingprocess to map logical block addresses to physical block addresses. Forexample, the first level of mapping illustratively uses the A2H tableand the second level of mapping uses the HMD table, with the A2H and HMDtables corresponding to respective logical and physical layers of thecontent-based signature mapping within the content addressable storagesystem 805. The HMD table or a given portion thereof in some embodimentsdisclosed herein is more particularly referred to as an H2D table.

The first level of mapping using the A2H table associates logicaladdresses of respective data pages with respective content-basedsignatures of those data pages. This is also referred to as logicallayer mapping.

The second level of mapping using the HMD table associates respectiveones of the content-based signatures with respective physical storagelocations in one or more of the storage devices 106. This is alsoreferred to as physical layer mapping.

Examples of these and other metadata structures utilized in illustrativeembodiments include the A2H, H2D, HMD and PLB tables of respective FIGS.10A through 10D. In some embodiments, the A2H and H2D tables areutilized primarily by the control modules 808C, while the HMD and PLBtables are utilized primarily by the data modules 808D.

For a given write request, hash metadata comprising at least a subset ofthe above-noted tables is updated in conjunction with the processing ofthat write request.

The A2H, H2D, HMD and PLB tables described above are examples of whatare more generally referred to herein as “mapping tables” of respectivedistinct types. Other types and arrangements of mapping tables or othercontent-based signature mapping information may be used in otherembodiments.

Such mapping tables are still more generally referred to herein as“metadata structures” of the content addressable storage system 805. Itshould be noted that additional or alternative metadata structures canbe used in other embodiments. References herein to particular tables ofparticular types, such as A2H, H2D, HMD and PLB tables, and theirrespective configurations, should be considered non-limiting and arepresented by way of illustrative example only. Such metadata structurescan be implemented in numerous alternative configurations with differentarrangements of fields and entries in other embodiments.

The logical block addresses or LBAs of a logical layer of the storagesystem 805 correspond to respective physical blocks of a physical layerof the storage system 805. The user data pages of the logical layer areorganized by LBA and have reference via respective content-basedsignatures to particular physical blocks of the physical layer.

Each of the physical blocks has an associated reference count that ismaintained within the storage system 805. The reference count for agiven physical block indicates the number of logical blocks that pointto that same physical block.

In releasing logical address space in the storage system, adereferencing operation is generally executed for each of the LBAs beingreleased. More particularly, the reference count of the correspondingphysical block is decremented. A reference count of zero indicates thatthere are no longer any logical blocks that reference the correspondingphysical block, and so that physical block can be released.

It should also be understood that the particular arrangement of storagecontroller processing modules 808C, 808D, 808R and 808M as shown in theFIG. 8 embodiment is presented by way of example only. Numerousalternative arrangements of processing modules of a distributed storagecontroller may be used to implement functionality for memory-efficientprocessing of RAID metadata bitmaps in a clustered storage system inother embodiments.

Additional examples of content addressable storage functionalityimplemented in some embodiments by control modules 808C, data modules808D, routing modules 808R and management module(s) 808M of distributedstorage controller 808 can be found in U.S. Pat. No. 9,104,326, entitled“Scalable Block Data Storage Using Content Addressing,” which isincorporated by reference herein. Alternative arrangements of these andother storage node processing modules of a distributed storagecontroller in a content addressable storage system can be used in otherembodiments.

The storage nodes 815 are illustratively configured to computecontent-based signatures for respective data pages or other types ofdata blocks. The content-based signatures are illustratively part of adesignated content-based signature space of the storage system 805, withdifferent portions of the content-based signature space of the storagesystem 805 being assigned to respective different ones of the storagenodes 815.

As indicated previously, the content-based signature space of thestorage system 805 illustratively comprises one of a hash handle spaceof the storage system and a hash digest space of the storage system 805.

The above-described embodiments can be implemented in storage systems ofthe type described elsewhere herein, and accordingly are not limited touse in content addressable storage systems. Again, a wide variety ofdifferent RAID and parity computation arrangements can be used.

In some embodiments, the storage system comprises an XtremIO™ storagearray or other type of content addressable storage system suitablymodified to incorporate functionality for memory-efficient processing ofRAID metadata bitmaps as disclosed herein.

It is to be appreciated that the particular advantages described aboveand elsewhere herein are associated with particular illustrativeembodiments and need not be present in other embodiments. Also, theparticular types of information processing system features andfunctionality as illustrated in the drawings and described above areexemplary only, and numerous other arrangements may be used in otherembodiments.

Illustrative embodiments of processing platforms utilized to implementhost devices and storage systems with functionality for memory-efficientprocessing of RAID metadata bitmaps will now be described in greaterdetail with reference to FIGS. 11 and 12. Although described in thecontext of system 100, these platforms may also be used to implement atleast portions of other information processing systems in otherembodiments.

FIG. 11 shows an example processing platform comprising cloudinfrastructure 1100. The cloud infrastructure 1100 comprises acombination of physical and virtual processing resources that may beutilized to implement at least a portion of the information processingsystem 100. The cloud infrastructure 1100 comprises multiple virtualmachines (VMs) and/or container sets 1102-1, 1102-2, . . . 1102-Limplemented using virtualization infrastructure 1104. The virtualizationinfrastructure 1104 runs on physical infrastructure 1105, andillustratively comprises one or more hypervisors and/or operating systemlevel virtualization infrastructure. The operating system levelvirtualization infrastructure illustratively comprises kernel controlgroups of a Linux operating system or other type of operating system.

The cloud infrastructure 1100 further comprises sets of applications1110-1, 1110-2, . . . 1110-L running on respective ones of theVMs/container sets 1102-1, 1102-2, . . . 1102-L under the control of thevirtualization infrastructure 1104. The VMs/container sets 1102 maycomprise respective VMs, respective sets of one or more containers, orrespective sets of one or more containers running in VMs.

In some implementations of the FIG. 11 embodiment, the VMs/containersets 1102 comprise respective VMs implemented using virtualizationinfrastructure 1104 that comprises at least one hypervisor. Suchimplementations can provide functionality for memory-efficientprocessing of RAID metadata bitmaps using processes running on one ormore of the VMs. For example, each of the VMs can implement suchfunctionality using one or more processes running on that particular VM.

An example of a hypervisor platform that may be used to implement ahypervisor within the virtualization infrastructure 1104 is the VMware®vSphere® which may have an associated virtual infrastructure managementsystem such as the VMware® vCenter™. The underlying physical machinesmay comprise one or more distributed processing platforms that includeone or more storage systems.

In other implementations of the FIG. 11 embodiment, the VMs/containersets 1102 comprise respective containers implemented usingvirtualization infrastructure 1104 that provides operating system levelvirtualization functionality, such as support for Docker containersrunning on bare metal hosts, or Docker containers running on VMs. Thecontainers are illustratively implemented using respective kernelcontrol groups of the operating system. Such implementations can providefunctionality for memory-efficient processing of RAID metadata bitmapsusing processes running on different ones of the containers. Forexample, a container host device supporting multiple containers of oneor more container sets can implement one or more instances of suchfunctionality.

As is apparent from the above, one or more of the processing modules orother components of system 100 may each run on a computer, server,storage device or other processing platform element. A given suchelement may be viewed as an example of what is more generally referredto herein as a “processing device.” The cloud infrastructure 1100 shownin FIG. 11 may represent at least a portion of one processing platform.Another example of such a processing platform is processing platform1200 shown in FIG. 12.

The processing platform 1200 in this embodiment comprises a portion ofsystem 100 or 200 and includes a plurality of processing devices,denoted 1202-1, 1202-2, 1202-3, . . . 1202-K, which communicate with oneanother over a network 1204.

The network 1204 may comprise any type of network, including by way ofexample a global computer network such as the Internet, a WAN, a LAN, asatellite network, a telephone or cable network, a cellular network, awireless network such as a WiFi or WiMAX network, or various portions orcombinations of these and other types of networks.

The processing device 1202-1 in the processing platform 1200 comprises aprocessor 1210 coupled to a memory 1212.

The processor 1210 may comprise a microprocessor, a microcontroller, anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA) or other type of processing circuitry, as well asportions or combinations of such circuitry elements.

The memory 1212 may comprise random access memory (RAM), read-onlymemory (ROM), flash memory or other types of memory, in any combination.The memory 1212 and other memories disclosed herein should be viewed asillustrative examples of what are more generally referred to as“processor-readable storage media” storing executable program code ofone or more software programs.

Articles of manufacture comprising such processor-readable storage mediaare considered illustrative embodiments. A given such article ofmanufacture may comprise, for example, a storage array, a storage diskor an integrated circuit containing RAM, ROM, flash memory or otherelectronic memory, or any of a wide variety of other types of computerprogram products. The term “article of manufacture” as used hereinshould be understood to exclude transitory, propagating signals.Numerous other types of computer program products comprisingprocessor-readable storage media can be used.

Also included in the processing device 1202-1 is network interfacecircuitry 1214, which is used to interface the processing device withthe network 1204 and other system components and may compriseconventional transceivers.

The other processing devices 1202 of the processing platform 1200 areassumed to be configured in a manner similar to that shown forprocessing device 1202-1 in the figure.

Again, the particular processing platform 1200 shown in the figure ispresented by way of example only, and system 100 or 200 may includeadditional or alternative processing platforms, as well as numerousdistinct processing platforms in any combination, with each suchplatform comprising one or more computers, servers, storage devices orother processing devices.

For example, other processing platforms used to implement illustrativeembodiments can comprise converged infrastructure such as VxRail™,VxRack™, VxRack™ FLEX, VxBlock™, or Vblock® converged infrastructurefrom Dell EMC.

It should therefore be understood that in other embodiments differentarrangements of additional or alternative elements may be used. At leasta subset of these elements may be collectively implemented on a commonprocessing platform, or each such element may be implemented on aseparate processing platform.

As indicated previously, components of an information processing systemas disclosed herein can be implemented at least in part in the form ofone or more software programs stored in memory and executed by aprocessor of a processing device. For example, at least portions of thefunctionality for memory-efficient processing of RAID metadata bitmapsas disclosed herein are illustratively implemented in the form ofsoftware running on one or more processing devices.

It should again be emphasized that the above-described embodiments arepresented for purposes of illustration only. Many variations and otheralternative embodiments may be used. For example, the disclosedtechniques are applicable to a wide variety of other types ofinformation processing systems, host devices, storage systems, storagenodes, storage devices, storage controllers, RAID arrangements, andtechniques for memory-efficient processing of RAID metadata bitmaps.Also, the particular configurations of system and device elements andassociated processing operations illustratively shown in the drawingscan be varied in other embodiments. Moreover, the various assumptionsmade above in the course of describing the illustrative embodimentsshould also be viewed as exemplary rather than as requirements orlimitations of the disclosure. Numerous other alternative embodimentswithin the scope of the appended claims will be readily apparent tothose skilled in the art.

What is claimed is:
 1. An apparatus comprising: a storage systemcomprising a plurality of storage nodes each comprising one or morestorage devices; each of the storage nodes further comprising aprocessor coupled to a memory; wherein the storage system is configuredto store data blocks across the storage devices of the storage nodesutilizing a redundant array of independent disks (RAID) arrangement; atleast a given one of the storage nodes being configured: to store aplurality of RAID metadata bitmaps in persistent storage of the storagenode so as to be available for a recovery operation in the event of adetected failure; to identify a particular subset of the RAID metadatabitmaps to be updated in conjunction with an additional operation otherthan the recovery operation; and to temporarily store the identifiedsubset of the RAID metadata bitmaps in the memory of the storage node ina manner determined based at least in part on an operation type of theadditional operation.
 2. The apparatus of claim 1 wherein the RAIDarrangement includes parity information supporting at least one recoveryoption for reconstructing the data blocks of at least one of the storagedevices responsive to a failure of that storage device.
 3. The apparatusof claim 1 wherein each of the storage nodes further comprises a set ofprocessing modules configured to communicate over one or more networkswith corresponding sets of processing modules on other ones of thestorage nodes.
 4. The apparatus of claim 3 wherein the sets ofprocessing modules comprise respective servers that collectivelyimplement at least a portion of a distributed storage controller of thestorage system.
 5. The apparatus of claim 3 wherein the sets ofprocessing modules of the storage nodes each comprise at least one datamodule and at least one control module.
 6. The apparatus of claim 1wherein the given storage node in reading data blocks from its one ormore storage devices is further configured to read the data blocks fromat least one designated RAID stripe.
 7. The apparatus of claim 1 whereindifferent ones of the RAID metadata bitmaps indicate at least free ortaken status for different subsets of the data blocks stored across thestorage devices of the storage nodes, and wherein a given one of theRAID metadata bitmaps comprises a plurality of entries for respectiveones of its associated data blocks with each such entry indicating whichof a plurality of possible states applies to the corresponding datablock wherein the possible states include at least a free state and ataken state.
 8. The apparatus of claim 7 wherein the possible statesfurther comprise at least one additional state including a not paritystate.
 9. The apparatus of claim 1 wherein identifying a particularsubset of the RAID metadata bitmaps to be updated in conjunction with anadditional operation other than the recovery operation comprises:detecting a write cache emptying operation; and determining particularRAID stripes to be written as part of the write cache emptyingoperation; selecting one or more of the particular RAID stripes; andidentifying the particular subset of the RAID metadata bitmaps as one ormore of the RAID metadata bitmaps associated with the selected one ormore RAID stripes.
 10. The apparatus of claim 9 wherein selecting one ormore of the RAID stripes comprises selecting the one or more RAIDstripes based at least in part on amounts of free data blocks inrespective ones of the RAID stripes.
 11. The apparatus of claim 1wherein the given storage node is further configured: to selectparticular ones of a plurality of RAID stripes to be rebuilt in arebuild operation; and to read one or more of the RAID metadata bitmapsassociated with the selected RAID stripes into the memory; and toperform corresponding portions of the rebuild operation; to write theRAID metadata bitmaps to the persistent storage; to repeat theselecting, reading, performing and writing until the rebuild operationis complete.
 12. The apparatus of claim 1 wherein the given storage nodeis further configured: to delay verification of at least one of the RAIDmetadata bitmaps in conjunction with a read operation; and to performthe verification of said at least one of the RAID metadata bitmaps inconjunction with a subsequent writing thereof.
 13. The apparatus ofclaim 1 wherein identifying a particular subset of the RAID metadatabitmaps to be updated in conjunction with an additional operation otherthan the recovery operation comprises: detecting an operation thatinvolves decrementing reference counts of a subset of the data blocks tozero; and identifying the particular subset of the RAID metadata bitmapsas one or more of the RAID metadata bitmaps associated with theoperation that involves decrementing reference counts of a subset of thedata blocks to zero.
 14. The apparatus of claim 1 wherein the givenstorage node is further configured: to identify a subset of the datablocks to be read in conjunction with a reconstruction operation tocorrect data block degradations; and to launch multiple reads ofrespective RAID metadata bitmaps in parallel with performing reads ofthe identified subset of data blocks.
 15. A method comprising:configuring a storage system to include a plurality of storage nodeseach comprising one or more storage devices, each of the storage nodesfurther comprising a processor coupled to a memory; storing data blocksacross the storage devices of the storage nodes utilizing a redundantarray of independent disks (RAID) arrangement; and for at least a givenone of the storage nodes: storing a plurality of RAID metadata bitmapsin persistent storage of the storage node so as to be available for arecovery operation in the event of a detected failure; identifying aparticular subset of the RAID metadata bitmaps to be updated inconjunction with an additional operation other than the recoveryoperation; and temporarily storing the identified subset of the RAIDmetadata bitmaps in the memory of the storage node in a mannerdetermined based at least in part on an operation type of the additionaloperation.
 16. The method of claim 15 wherein different ones of the RAIDmetadata bitmaps indicate at least free or taken status for differentsubsets of the data blocks stored across the storage devices of thestorage nodes, and wherein a given one of the RAID metadata bitmapscomprises a plurality of entries for respective ones of its associateddata blocks with each such entry indicating which of a plurality ofpossible states applies to the corresponding data block wherein thepossible states include at least a free state and a taken state.
 17. Themethod of claim 15 wherein identifying a particular subset of the RAIDmetadata bitmaps to be updated in conjunction with an additionaloperation other than the recovery operation comprises: detecting a writecache emptying operation; and determining particular RAID stripes to bewritten as part of the write cache emptying operation; selecting one ormore of the particular RAID stripes; and identifying the particularsubset of the RAID metadata bitmaps as one or more of the RAID metadatabitmaps associated with the selected one or more RAID stripes.
 18. Acomputer program product comprising a non-transitory processor-readablestorage medium having stored therein program code of one or moresoftware programs, wherein the program code when executed by a storagesystem comprising a plurality of storage nodes each comprising one ormore storage devices, each of the storage nodes further comprising aprocessor coupled to a memory, causes the storage system: to store datablocks across the storage devices of the storage nodes utilizing aredundant array of independent disks (RAID) arrangement; and for atleast a given one of the storage nodes: to store a plurality of RAIDmetadata bitmaps in persistent storage of the storage node so as to beavailable for a recovery operation in the event of a detected failure;to identify a particular subset of the RAID metadata bitmaps to beupdated in conjunction with an additional operation other than therecovery operation; and to temporarily store the identified subset ofthe RAID metadata bitmaps in the memory of the storage node in a mannerdetermined based at least in part on an operation type of the additionaloperation.
 19. The computer program product of claim 18 whereindifferent ones of the RAID metadata bitmaps indicate at least free ortaken status for different subsets of the data blocks stored across thestorage devices of the storage nodes, and wherein a given one of theRAID metadata bitmaps comprises a plurality of entries for respectiveones of its associated data blocks with each such entry indicating whichof a plurality of possible states applies to the corresponding datablock wherein the possible states include at least a free state and ataken state.
 20. The computer program product of claim 18 whereinidentifying a particular subset of the RAID metadata bitmaps to beupdated in conjunction with an additional operation other than therecovery operation comprises: detecting a write cache emptyingoperation; and determining particular RAID stripes to be written as partof the write cache emptying operation; selecting one or more of theparticular RAID stripes; and identifying the particular subset of theRAID metadata bitmaps as one or more of the RAID metadata bitmapsassociated with the selected one or more RAID stripes.